db-LaCAM: Fast and Scalable Multi-Robot Kinodynamic Motion Planning with Discontinuity-Bounded Search and Lightweight MAPF

TL;DR

This paper introduces db-LaCAM, a fast, scalable multi-robot motion planner that combines MAPF algorithms with kinodynamic planning, enabling efficient planning for up to 50 robots with dynamic constraints.

Contribution

The paper presents a novel discontinuity-bounded kinodynamic planner that integrates motion primitives with MAPF techniques for improved scalability and speed.

Findings

Scales efficiently to 50 robots in complex environments

Achieves up to ten times faster runtime than existing methods

Maintains solution quality comparable to state-of-the-art planners

Abstract

State-of-the-art multi-robot kinodynamic motion planners struggle to handle more than a few robots due to high computational burden, which limits their scalability and results in slow planning time. In this work, we combine the scalability and speed of modern multi-agent path finding (MAPF) algorithms with the dynamic-awareness of kinodynamic planners to address these limitations. To this end, we propose discontinuity-Bounded LaCAM (db-LaCAM), a planner that utilizes a precomputed set of motion primitives that respect robot dynamics to generate horizon-length motion sequences, while allowing a user-defined discontinuity between successive motions. The planner db-LaCAM is resolution-complete with respect to motion primitives and supports arbitrary robot dynamics. Extensive experiments demonstrate that db-LaCAM scales efficiently to scenarios with up to 50 robots, achieving up to ten times faster runtime compared to state-of-the-art planners, while maintaining comparable solution quality. The approach is validated in both 2D and 3D environments with dynamics such as the unicycle and 3D double integrator. We demonstrate the safe execution of trajectories planned with db-LaCAM in two distinct physical experiments involving teams of flying robots and car-with-trailer robots.